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Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning

Shaoxuan Yun, Ying Chen*
School of Computing Science, Beijing Information Science and Technology University, Beijing, 100101, China
* Corresponding Author: Ying Chen. Email:
(This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1815-1835. https://doi.org/10.32604/cmes.2022.022797

Received 26 March 2022; Accepted 26 May 2022; Issue published 20 September 2022

Abstract

Nowadays, with the widespread application of the Internet of Things (IoT), mobile devices are renovating our lives. The data generated by mobile devices has reached a massive level. The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load. Mobile Edge Computing (MEC) has been proposed to solve these problems. Because of limited computation ability and battery capacity, tasks can be executed in the MEC server. However, how to schedule those tasks becomes a challenge, and is the main topic of this piece. In this paper, we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC. In view of the advantages of deep learning, we propose a Deep Learning-Based Traffic Scheduling Approach (DLTSA). We translate the scheduling problem into a classification problem. Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.

Keywords

Mobile Edge Computing (MEC); traffic scheduling; deep learning; Internet of Things (IoT)

Cite This Article

Yun, S., Chen, Y. (2023). Intelligent Traffic Scheduling for Mobile Edge Computing in IoT via Deep Learning. CMES-Computer Modeling in Engineering & Sciences, 134(3), 1815–1835.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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